Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations850
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory337.7 KiB
Average record size in memory406.8 B

Variable types

Categorical6
Numeric7

Alerts

year has constant value "1970" Constant
city/district is highly overall correlated with landfill_capacity_(tons) and 3 other fieldsHigh correlation
landfill_capacity_(tons) is highly overall correlated with city/district and 2 other fieldsHigh correlation
landfill_location_(lat,_long) is highly overall correlated with city/district and 3 other fieldsHigh correlation
landfill_name is highly overall correlated with city/district and 3 other fieldsHigh correlation
population_density_(people/km²) is highly overall correlated with city/district and 2 other fieldsHigh correlation
city/district is uniformly distributed Uniform
waste_type is uniformly distributed Uniform
landfill_name is uniformly distributed Uniform
landfill_location_(lat,_long) is uniformly distributed Uniform
awareness_campaigns_count has 48 (5.6%) zeros Zeros

Reproduction

Analysis started2025-08-10 12:20:50.191510
Analysis finished2025-08-10 12:20:58.016468
Duration7.82 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

city/district
Categorical

High correlation  Uniform 

Distinct34
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
Mumbai
 
25
Delhi
 
25
Bengaluru
 
25
Chennai
 
25
Kolkata
 
25
Other values (29)
725 

Length

Max length18
Median length10
Mean length7.3529412
Min length4

Characters and Unicode

Total characters6,250
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowMumbai
3rd rowMumbai
4th rowMumbai
5th rowMumbai

Common Values

ValueCountFrequency (%)
Mumbai 25
 
2.9%
Delhi 25
 
2.9%
Bengaluru 25
 
2.9%
Chennai 25
 
2.9%
Kolkata 25
 
2.9%
Hyderabad 25
 
2.9%
Pune 25
 
2.9%
Ahmedabad 25
 
2.9%
Jaipur 25
 
2.9%
Lucknow 25
 
2.9%
Other values (24) 600
70.6%

Length

2025-08-10T17:50:58.088990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 25
 
2.9%
delhi 25
 
2.9%
bengaluru 25
 
2.9%
chennai 25
 
2.9%
kolkata 25
 
2.9%
hyderabad 25
 
2.9%
pune 25
 
2.9%
ahmedabad 25
 
2.9%
jaipur 25
 
2.9%
lucknow 25
 
2.9%
Other values (24) 600
70.6%

Most occurring characters

ValueCountFrequency (%)
a 1200
19.2%
r 500
 
8.0%
u 425
 
6.8%
i 375
 
6.0%
n 350
 
5.6%
h 325
 
5.2%
d 275
 
4.4%
o 250
 
4.0%
t 250
 
4.0%
e 250
 
4.0%
Other values (29) 2050
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1200
19.2%
r 500
 
8.0%
u 425
 
6.8%
i 375
 
6.0%
n 350
 
5.6%
h 325
 
5.2%
d 275
 
4.4%
o 250
 
4.0%
t 250
 
4.0%
e 250
 
4.0%
Other values (29) 2050
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1200
19.2%
r 500
 
8.0%
u 425
 
6.8%
i 375
 
6.0%
n 350
 
5.6%
h 325
 
5.2%
d 275
 
4.4%
o 250
 
4.0%
t 250
 
4.0%
e 250
 
4.0%
Other values (29) 2050
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1200
19.2%
r 500
 
8.0%
u 425
 
6.8%
i 375
 
6.0%
n 350
 
5.6%
h 325
 
5.2%
d 275
 
4.4%
o 250
 
4.0%
t 250
 
4.0%
e 250
 
4.0%
Other values (29) 2050
32.8%

waste_type
Categorical

Uniform 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size54.4 KiB
Plastic
170 
Organic
170 
E-Waste
170 
Construction
170 
Hazardous
170 

Length

Max length12
Median length7
Mean length8.4
Min length7

Characters and Unicode

Total characters7,140
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlastic
2nd rowOrganic
3rd rowE-Waste
4th rowConstruction
5th rowHazardous

Common Values

ValueCountFrequency (%)
Plastic 170
20.0%
Organic 170
20.0%
E-Waste 170
20.0%
Construction 170
20.0%
Hazardous 170
20.0%

Length

2025-08-10T17:50:58.169719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-10T17:50:58.240399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
plastic 170
20.0%
organic 170
20.0%
e-waste 170
20.0%
construction 170
20.0%
hazardous 170
20.0%

Most occurring characters

ValueCountFrequency (%)
a 850
11.9%
t 680
 
9.5%
s 680
 
9.5%
c 510
 
7.1%
i 510
 
7.1%
o 510
 
7.1%
n 510
 
7.1%
r 510
 
7.1%
u 340
 
4.8%
l 170
 
2.4%
Other values (11) 1870
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 850
11.9%
t 680
 
9.5%
s 680
 
9.5%
c 510
 
7.1%
i 510
 
7.1%
o 510
 
7.1%
n 510
 
7.1%
r 510
 
7.1%
u 340
 
4.8%
l 170
 
2.4%
Other values (11) 1870
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 850
11.9%
t 680
 
9.5%
s 680
 
9.5%
c 510
 
7.1%
i 510
 
7.1%
o 510
 
7.1%
n 510
 
7.1%
r 510
 
7.1%
u 340
 
4.8%
l 170
 
2.4%
Other values (11) 1870
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 850
11.9%
t 680
 
9.5%
s 680
 
9.5%
c 510
 
7.1%
i 510
 
7.1%
o 510
 
7.1%
n 510
 
7.1%
r 510
 
7.1%
u 340
 
4.8%
l 170
 
2.4%
Other values (11) 1870
26.2%
Distinct807
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5262.2494
Minimum511
Maximum9980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:58.331187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum511
5-th percentile875.05
Q12865.75
median5283
Q37757.25
95-th percentile9507.8
Maximum9980
Range9469
Interquartile range (IQR)4891.5

Descriptive statistics

Standard deviation2786.9847
Coefficient of variation (CV)0.52961852
Kurtosis-1.2255036
Mean5262.2494
Median Absolute Deviation (MAD)2442
Skewness-0.023816513
Sum4472912
Variance7767283.9
MonotonicityNot monotonic
2025-08-10T17:50:58.434892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5732 3
 
0.4%
7540 3
 
0.4%
1887 2
 
0.2%
4466 2
 
0.2%
2294 2
 
0.2%
6409 2
 
0.2%
3125 2
 
0.2%
4118 2
 
0.2%
4487 2
 
0.2%
1942 2
 
0.2%
Other values (797) 828
97.4%
ValueCountFrequency (%)
511 1
0.1%
512 1
0.1%
525 1
0.1%
526 1
0.1%
528 1
0.1%
545 2
0.2%
549 2
0.2%
552 1
0.1%
558 1
0.1%
577 1
0.1%
ValueCountFrequency (%)
9980 1
0.1%
9976 1
0.1%
9954 1
0.1%
9946 1
0.1%
9929 1
0.1%
9903 1
0.1%
9883 1
0.1%
9869 1
0.1%
9857 1
0.1%
9848 1
0.1%

recycling_rate_(%)
Real number (ℝ)

Distinct56
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.076471
Minimum30
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:58.531475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile32
Q143
median56
Q371
95-th percentile82
Maximum85
Range55
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.129994
Coefficient of variation (CV)0.28260322
Kurtosis-1.1991203
Mean57.076471
Median Absolute Deviation (MAD)14
Skewness0.026325494
Sum48515
Variance260.17671
MonotonicityNot monotonic
2025-08-10T17:50:58.628455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 26
 
3.1%
53 22
 
2.6%
69 21
 
2.5%
38 21
 
2.5%
55 20
 
2.4%
48 19
 
2.2%
54 19
 
2.2%
52 19
 
2.2%
61 19
 
2.2%
58 18
 
2.1%
Other values (46) 646
76.0%
ValueCountFrequency (%)
30 15
1.8%
31 15
1.8%
32 16
1.9%
33 16
1.9%
34 15
1.8%
35 12
1.4%
36 26
3.1%
37 14
1.6%
38 21
2.5%
39 17
2.0%
ValueCountFrequency (%)
85 17
2.0%
84 12
1.4%
83 12
1.4%
82 10
1.2%
81 18
2.1%
80 11
1.3%
79 18
2.1%
78 17
2.0%
77 17
2.0%
76 16
1.9%

population_density_(people/km²)
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13489.706
Minimum2335
Maximum24032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:58.716538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2335
5-th percentile2929
Q17927
median12579.5
Q319087
95-th percentile23465
Maximum24032
Range21697
Interquartile range (IQR)11160

Descriptive statistics

Standard deviation6631.0815
Coefficient of variation (CV)0.49156605
Kurtosis-1.2359237
Mean13489.706
Median Absolute Deviation (MAD)5727
Skewness-0.066472444
Sum11466250
Variance43971242
MonotonicityNot monotonic
2025-08-10T17:50:58.799872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2929 50
 
5.9%
11191 25
 
2.9%
14376 25
 
2.9%
21430 25
 
2.9%
18195 25
 
2.9%
7927 25
 
2.9%
12547 25
 
2.9%
9464 25
 
2.9%
17335 25
 
2.9%
2335 25
 
2.9%
Other values (23) 575
67.6%
ValueCountFrequency (%)
2335 25
2.9%
2929 50
5.9%
4071 25
2.9%
4629 25
2.9%
6465 25
2.9%
7118 25
2.9%
7178 25
2.9%
7927 25
2.9%
7976 25
2.9%
8678 25
2.9%
ValueCountFrequency (%)
24032 25
2.9%
23465 25
2.9%
23232 25
2.9%
22866 25
2.9%
21598 25
2.9%
21430 25
2.9%
19454 25
2.9%
19181 25
2.9%
19087 25
2.9%
18697 25
2.9%
Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4
Minimum5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:58.866619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median7
Q39
95-th percentile10
Maximum10
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7221621
Coefficient of variation (CV)0.2327246
Kurtosis-1.3048067
Mean7.4
Median Absolute Deviation (MAD)2
Skewness0.056862826
Sum6290
Variance2.9658422
MonotonicityNot monotonic
2025-08-10T17:50:58.926728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 159
18.7%
6 151
17.8%
9 148
17.4%
7 133
15.6%
8 132
15.5%
10 127
14.9%
ValueCountFrequency (%)
5 159
18.7%
6 151
17.8%
7 133
15.6%
8 132
15.5%
9 148
17.4%
10 127
14.9%
ValueCountFrequency (%)
10 127
14.9%
9 148
17.4%
8 132
15.5%
7 133
15.6%
6 151
17.8%
5 159
18.7%

disposal_method
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size55.5 KiB
Incineration
218 
Recycling
213 
Landfill
210 
Composting
209 

Length

Max length12
Median length10
Mean length9.7682353
Min length8

Characters and Unicode

Total characters8,303
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComposting
2nd rowComposting
3rd rowIncineration
4th rowLandfill
5th rowRecycling

Common Values

ValueCountFrequency (%)
Incineration 218
25.6%
Recycling 213
25.1%
Landfill 210
24.7%
Composting 209
24.6%

Length

2025-08-10T17:50:59.012300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-10T17:50:59.076421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
incineration 218
25.6%
recycling 213
25.1%
landfill 210
24.7%
composting 209
24.6%

Most occurring characters

ValueCountFrequency (%)
n 1286
15.5%
i 1068
12.9%
c 644
 
7.8%
o 636
 
7.7%
l 633
 
7.6%
e 431
 
5.2%
a 428
 
5.2%
t 427
 
5.1%
g 422
 
5.1%
I 218
 
2.6%
Other values (10) 2110
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1286
15.5%
i 1068
12.9%
c 644
 
7.8%
o 636
 
7.7%
l 633
 
7.6%
e 431
 
5.2%
a 428
 
5.2%
t 427
 
5.1%
g 422
 
5.1%
I 218
 
2.6%
Other values (10) 2110
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1286
15.5%
i 1068
12.9%
c 644
 
7.8%
o 636
 
7.7%
l 633
 
7.6%
e 431
 
5.2%
a 428
 
5.2%
t 427
 
5.1%
g 422
 
5.1%
I 218
 
2.6%
Other values (10) 2110
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1286
15.5%
i 1068
12.9%
c 644
 
7.8%
o 636
 
7.7%
l 633
 
7.6%
e 431
 
5.2%
a 428
 
5.2%
t 427
 
5.1%
g 422
 
5.1%
I 218
 
2.6%
Other values (10) 2110
25.4%
Distinct780
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2778.4588
Minimum503
Maximum4999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:59.217880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum503
5-th percentile762.9
Q11647.5
median2853
Q33855
95-th percentile4733.2
Maximum4999
Range4496
Interquartile range (IQR)2207.5

Descriptive statistics

Standard deviation1276.3256
Coefficient of variation (CV)0.4593646
Kurtosis-1.1574327
Mean2778.4588
Median Absolute Deviation (MAD)1097
Skewness-0.057412188
Sum2361690
Variance1629007.1
MonotonicityNot monotonic
2025-08-10T17:50:59.315254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3131 3
 
0.4%
4725 3
 
0.4%
1093 3
 
0.4%
3000 3
 
0.4%
1084 3
 
0.4%
4307 3
 
0.4%
3888 2
 
0.2%
4938 2
 
0.2%
1687 2
 
0.2%
1135 2
 
0.2%
Other values (770) 824
96.9%
ValueCountFrequency (%)
503 1
0.1%
509 1
0.1%
510 1
0.1%
516 2
0.2%
527 1
0.1%
533 1
0.1%
549 1
0.1%
550 2
0.2%
553 1
0.1%
557 1
0.1%
ValueCountFrequency (%)
4999 1
0.1%
4993 1
0.1%
4988 1
0.1%
4978 2
0.2%
4975 1
0.1%
4966 1
0.1%
4958 1
0.1%
4952 2
0.2%
4944 1
0.1%
4941 1
0.1%

awareness_campaigns_count
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9047059
Minimum0
Maximum20
Zeros48
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:59.391645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0707724
Coefficient of variation (CV)0.61291798
Kurtosis-1.2160041
Mean9.9047059
Median Absolute Deviation (MAD)5
Skewness-0.038929224
Sum8419
Variance36.854277
MonotonicityNot monotonic
2025-08-10T17:50:59.466731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11 50
 
5.9%
15 49
 
5.8%
0 48
 
5.6%
14 46
 
5.4%
17 45
 
5.3%
12 44
 
5.2%
1 43
 
5.1%
6 43
 
5.1%
13 42
 
4.9%
5 42
 
4.9%
Other values (11) 398
46.8%
ValueCountFrequency (%)
0 48
5.6%
1 43
5.1%
2 40
4.7%
3 38
4.5%
4 41
4.8%
5 42
4.9%
6 43
5.1%
7 37
4.4%
8 30
3.5%
9 28
3.3%
ValueCountFrequency (%)
20 37
4.4%
19 39
4.6%
18 28
3.3%
17 45
5.3%
16 41
4.8%
15 49
5.8%
14 46
5.4%
13 42
4.9%
12 44
5.2%
11 50
5.9%

landfill_name
Categorical

High correlation  Uniform 

Distinct34
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
Mumbai Landfill
 
25
Delhi Landfill
 
25
Bengaluru Landfill
 
25
Chennai Landfill
 
25
Kolkata Landfill
 
25
Other values (29)
725 

Length

Max length27
Median length19
Mean length16.352941
Min length13

Characters and Unicode

Total characters13,900
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai Landfill
2nd rowMumbai Landfill
3rd rowMumbai Landfill
4th rowMumbai Landfill
5th rowMumbai Landfill

Common Values

ValueCountFrequency (%)
Mumbai Landfill 25
 
2.9%
Delhi Landfill 25
 
2.9%
Bengaluru Landfill 25
 
2.9%
Chennai Landfill 25
 
2.9%
Kolkata Landfill 25
 
2.9%
Hyderabad Landfill 25
 
2.9%
Pune Landfill 25
 
2.9%
Ahmedabad Landfill 25
 
2.9%
Jaipur Landfill 25
 
2.9%
Lucknow Landfill 25
 
2.9%
Other values (24) 600
70.6%

Length

2025-08-10T17:50:59.555579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
landfill 850
50.0%
mumbai 25
 
1.5%
delhi 25
 
1.5%
bengaluru 25
 
1.5%
chennai 25
 
1.5%
kolkata 25
 
1.5%
hyderabad 25
 
1.5%
pune 25
 
1.5%
ahmedabad 25
 
1.5%
jaipur 25
 
1.5%
Other values (25) 625
36.8%

Most occurring characters

ValueCountFrequency (%)
a 2050
14.7%
l 1900
13.7%
i 1225
8.8%
n 1200
8.6%
d 1125
 
8.1%
L 900
 
6.5%
f 850
 
6.1%
850
 
6.1%
r 500
 
3.6%
u 425
 
3.1%
Other values (31) 2875
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2050
14.7%
l 1900
13.7%
i 1225
8.8%
n 1200
8.6%
d 1125
 
8.1%
L 900
 
6.5%
f 850
 
6.1%
850
 
6.1%
r 500
 
3.6%
u 425
 
3.1%
Other values (31) 2875
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2050
14.7%
l 1900
13.7%
i 1225
8.8%
n 1200
8.6%
d 1125
 
8.1%
L 900
 
6.5%
f 850
 
6.1%
850
 
6.1%
r 500
 
3.6%
u 425
 
3.1%
Other values (31) 2875
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2050
14.7%
l 1900
13.7%
i 1225
8.8%
n 1200
8.6%
d 1125
 
8.1%
L 900
 
6.5%
f 850
 
6.1%
850
 
6.1%
r 500
 
3.6%
u 425
 
3.1%
Other values (31) 2875
20.7%

landfill_location_(lat,_long)
Categorical

High correlation  Uniform 

Distinct34
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size60.5 KiB
22.4265, 77.4931
 
25
30.2591, 91.9376
 
25
15.7581, 85.4837
 
25
29.4633, 80.8873
 
25
16.068, 72.6096
 
25
Other values (29)
725 

Length

Max length16
Median length16
Mean length15.764706
Min length15

Characters and Unicode

Total characters13,400
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22.4265, 77.4931
2nd row22.4265, 77.4931
3rd row22.4265, 77.4931
4th row22.4265, 77.4931
5th row22.4265, 77.4931

Common Values

ValueCountFrequency (%)
22.4265, 77.4931 25
 
2.9%
30.2591, 91.9376 25
 
2.9%
15.7581, 85.4837 25
 
2.9%
29.4633, 80.8873 25
 
2.9%
16.068, 72.6096 25
 
2.9%
34.069, 82.1518 25
 
2.9%
28.7237, 84.2278 25
 
2.9%
19.3868, 72.7845 25
 
2.9%
11.6909, 94.4267 25
 
2.9%
10.3904, 81.8808 25
 
2.9%
Other values (24) 600
70.6%

Length

2025-08-10T17:50:59.634302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22.4265 25
 
1.5%
77.4931 25
 
1.5%
30.2591 25
 
1.5%
91.9376 25
 
1.5%
15.7581 25
 
1.5%
85.4837 25
 
1.5%
29.4633 25
 
1.5%
80.8873 25
 
1.5%
16.068 25
 
1.5%
72.6096 25
 
1.5%
Other values (58) 1450
85.3%

Most occurring characters

ValueCountFrequency (%)
. 1700
12.7%
8 1375
10.3%
1 1175
8.8%
3 1050
7.8%
9 1000
7.5%
6 975
7.3%
2 975
7.3%
7 950
 
7.1%
4 950
 
7.1%
5 875
 
6.5%
Other values (3) 2375
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1700
12.7%
8 1375
10.3%
1 1175
8.8%
3 1050
7.8%
9 1000
7.5%
6 975
7.3%
2 975
7.3%
7 950
 
7.1%
4 950
 
7.1%
5 875
 
6.5%
Other values (3) 2375
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1700
12.7%
8 1375
10.3%
1 1175
8.8%
3 1050
7.8%
9 1000
7.5%
6 975
7.3%
2 975
7.3%
7 950
 
7.1%
4 950
 
7.1%
5 875
 
6.5%
Other values (3) 2375
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1700
12.7%
8 1375
10.3%
1 1175
8.8%
3 1050
7.8%
9 1000
7.5%
6 975
7.3%
2 975
7.3%
7 950
 
7.1%
4 950
 
7.1%
5 875
 
6.5%
Other values (3) 2375
17.7%

landfill_capacity_(tons)
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58934.618
Minimum22690
Maximum98646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2025-08-10T17:50:59.712691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22690
5-th percentile24927
Q145575
median61038.5
Q371127
95-th percentile98572
Maximum98646
Range75956
Interquartile range (IQR)25552

Descriptive statistics

Standard deviation19413.627
Coefficient of variation (CV)0.32940957
Kurtosis-0.59684302
Mean58934.618
Median Absolute Deviation (MAD)13246.5
Skewness0.053472764
Sum50094425
Variance3.7688892 × 108
MonotonicityNot monotonic
2025-08-10T17:50:59.812581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
45575 25
 
2.9%
74509 25
 
2.9%
52609 25
 
2.9%
65486 25
 
2.9%
80985 25
 
2.9%
34239 25
 
2.9%
49524 25
 
2.9%
69399 25
 
2.9%
71127 25
 
2.9%
80251 25
 
2.9%
Other values (24) 600
70.6%
ValueCountFrequency (%)
22690 25
2.9%
24927 25
2.9%
30373 25
2.9%
31477 25
2.9%
34239 25
2.9%
35432 25
2.9%
38471 25
2.9%
45318 25
2.9%
45575 25
2.9%
48016 25
2.9%
ValueCountFrequency (%)
98646 25
2.9%
98572 25
2.9%
83130 25
2.9%
82082 25
2.9%
80985 25
2.9%
80251 25
2.9%
79314 25
2.9%
74509 25
2.9%
71127 25
2.9%
69399 25
2.9%

year
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.8 KiB
1970
850 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3,400
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1970
2nd row1970
3rd row1970
4th row1970
5th row1970

Common Values

ValueCountFrequency (%)
1970 850
100.0%

Length

2025-08-10T17:50:59.898635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-10T17:50:59.943259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1970 850
100.0%

Most occurring characters

ValueCountFrequency (%)
1 850
25.0%
9 850
25.0%
7 850
25.0%
0 850
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 850
25.0%
9 850
25.0%
7 850
25.0%
0 850
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 850
25.0%
9 850
25.0%
7 850
25.0%
0 850
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 850
25.0%
9 850
25.0%
7 850
25.0%
0 850
25.0%

Interactions

2025-08-10T17:50:57.137314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.062149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.723424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.307524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.887563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.466379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.519021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.225710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.197487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.802368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.384849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.969941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.546206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.601506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.315533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.280139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.883445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.468773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.053840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.115361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.685683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.406903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.364976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.965406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.550561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.135655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.194897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.775440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.495781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.462803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.047203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.629496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.212364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.275959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.868059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.579871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.546031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.121592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.706188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.293294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.352425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.954678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.676075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:51.633266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.218236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:52.801281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:53.377658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:56.434751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-10T17:50:57.044309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-10T17:50:59.992510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
awareness_campaigns_countcity/districtcost_of_waste_management_(₹/ton)disposal_methodlandfill_capacity_(tons)landfill_location_(lat,_long)landfill_namemunicipal_efficiency_score_(1_10)population_density_(people/km²)recycling_rate_(%)waste_generated_(tons/day)waste_type
awareness_campaigns_count1.0000.000-0.0180.0000.0500.0000.0000.0380.048-0.018-0.0040.029
city/district0.0001.0000.0190.0000.9851.0001.0000.0630.9860.0450.0000.000
cost_of_waste_management_(₹/ton)-0.0180.0191.0000.000-0.0020.0190.019-0.070-0.031-0.0040.0420.000
disposal_method0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0490.0360.000
landfill_capacity_(tons)0.0500.985-0.0020.0001.0000.9850.9850.067-0.2810.0640.0360.000
landfill_location_(lat,_long)0.0001.0000.0190.0000.9851.0001.0000.0630.9860.0450.0000.000
landfill_name0.0001.0000.0190.0000.9851.0001.0000.0630.9860.0450.0000.000
municipal_efficiency_score_(1_10)0.0380.063-0.0700.0000.0670.0630.0631.000-0.086-0.0330.0170.000
population_density_(people/km²)0.0480.986-0.0310.000-0.2810.9860.986-0.0861.000-0.0530.0010.000
recycling_rate_(%)-0.0180.045-0.0040.0490.0640.0450.045-0.033-0.0531.000-0.0340.035
waste_generated_(tons/day)-0.0040.0000.0420.0360.0360.0000.0000.0170.001-0.0341.0000.000
waste_type0.0290.0000.0000.0000.0000.0000.0000.0000.0000.0350.0001.000

Missing values

2025-08-10T17:50:57.809815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-10T17:50:57.934415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

city/districtwaste_typewaste_generated_(tons/day)recycling_rate_(%)population_density_(people/km²)municipal_efficiency_score_(1_10)disposal_methodcost_of_waste_management_(₹/ton)awareness_campaigns_countlandfill_namelandfill_location_(lat,_long)landfill_capacity_(tons)year
0MumbaiPlastic661068111919Composting305614Mumbai Landfill22.4265, 77.4931455751970
1MumbaiOrganic118156111915Composting277812Mumbai Landfill22.4265, 77.4931455751970
2MumbaiE-Waste816253111918Incineration339013Mumbai Landfill22.4265, 77.4931455751970
3MumbaiConstruction892956111915Landfill149814Mumbai Landfill22.4265, 77.4931455751970
4MumbaiHazardous503244111917Recycling222116Mumbai Landfill22.4265, 77.4931455751970
5MumbaiPlastic745673111919Landfill31956Mumbai Landfill22.4265, 77.4931455751970
6MumbaiOrganic711837111916Composting368614Mumbai Landfill22.4265, 77.4931455751970
7MumbaiE-Waste9189571119110Landfill179112Mumbai Landfill22.4265, 77.4931455751970
8MumbaiConstruction860948111918Incineration168120Mumbai Landfill22.4265, 77.4931455751970
9MumbaiHazardous663271111916Incineration231112Mumbai Landfill22.4265, 77.4931455751970
city/districtwaste_typewaste_generated_(tons/day)recycling_rate_(%)population_density_(people/km²)municipal_efficiency_score_(1_10)disposal_methodcost_of_waste_management_(₹/ton)awareness_campaigns_countlandfill_namelandfill_location_(lat,_long)landfill_capacity_(tons)year
840GwaliorPlastic3556411128010Incineration40422Gwalior Landfill10.9566, 91.6565544601970
841GwaliorOrganic9726501128010Recycling44447Gwalior Landfill10.9566, 91.6565544601970
842GwaliorE-Waste530060112805Composting66915Gwalior Landfill10.9566, 91.6565544601970
843GwaliorConstruction756147112808Composting302418Gwalior Landfill10.9566, 91.6565544601970
844GwaliorHazardous427241112806Incineration41239Gwalior Landfill10.9566, 91.6565544601970
845GwaliorPlastic684242112808Recycling354615Gwalior Landfill10.9566, 91.6565544601970
846GwaliorOrganic5233381128010Recycling11465Gwalior Landfill10.9566, 91.6565544601970
847GwaliorE-Waste990341112807Landfill32604Gwalior Landfill10.9566, 91.6565544601970
848GwaliorConstruction754077112806Composting42207Gwalior Landfill10.9566, 91.6565544601970
849GwaliorHazardous248058112806Composting108119Gwalior Landfill10.9566, 91.6565544601970